Also, a distinguishing feature of our work is that each word has its own intent, rather than just one binary output for the intent (which appears similar to the ideas in [1]}). Several intents can be learnt together with the respective named entities, like navigational store hours lookup (store name is the entity), product lookup (the product is the entity), add to cart (product is the entity), store pickup (the store is the entity), page navigation (the page location is the entity, like navigating to a category page etc.), product styles (in which the style and the product would be the entities). All intents have their own token level loss functions for intent along with a common named entity recognition engine in a multi-task setting (This page has several interesting ideas about multi-task learning. In particular they use the term negative transfer in which one loss function cannibalizes the effect of the other and both then do poorly).
